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Results for “Time at Temperature”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Large-Strain Time-Temperature Equivalence and Adiabatic Heating of Polyethylene

Time-temperature equivalence is a well-known phenomenon in time-dependent material response, where rapid events at a moderate temperature are indistinguishable from some occurring at modest rates but elevated temperatures. However, there is as-yet little elucidation of how well this equivalence holds for substantial plastic strains. In this work, we demonstrate time-temperature equivalence over a large range in a previously studied high-density polyethylene formulation (HDPE). At strain-rates exceeding 0.1/s, adiabatic heating confounds the comparison of nominally isothermal material response, apparently violating time-temperature equivalence. Strain-rate jumps can be employed to access the instantaneous true strain rate without heating. Adiabatic heating effects were isolated by comparing a locus of isothermal instantaneous flow stress measurements from strain-rate jumps up to 1/s with the predicted equivalent states at 0.01/s and 0.001/s in compression. Excellent agreement between the isothermal jump condition locus and the quasi-static tests was observed up to 50% strain, yielding one effective isothermal plastic response for each material for a given time-temperature equivalent state. These results imply that time-temperature equivalence can be effectively used to predict the deformation response of polymers during extreme mechanical events (large strain and high strain-rate) from measurements taken at reduced temperatures and nominal strain-rates in the laboratory.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.

gated recurrent unit↗
DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series: Supporting Data

This dataset consists of soil temperature time series that were used to estimate soil thermal diffusivity and its uncertainty trough the probabilistic modelling approach developed and presented in the article named "Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series" and published in Earth Surface Dynamics. There are two compressed (.zip) files that contains synthetic (Synthetic_soiltemp_Teller.zip) and field (Field_soiltemp_Teller.zip) data. There is one MATLAB file that requires MATLAB to execute but any text editor can open it. The Synthetic_soiltemp_Teller.zip file includes 5 comma-delimited data files (.csv) each of which contains soil temperature time series generated through forward modeling (i.e., heat-conduction process in a heterogeneous medium using an explicit finite difference method) to mimic various types of temperature gradients, trend and fluctuations. These synthetic soil temperatures were used to investigate the impact of different environmental conditions on the uncertainty of thermal diffusivity estimates. The Field_soiltemp_Teller.zip file contains 28 comma-delimited data files (.csv) out of which (a) 27 files includes soil temperature time series recorded from 27 temperature probes located in a site along Teller Road about 40 km northwest of Nome, Alaska (64.72°N, 165.94°W), (b) one includes the name and coordinates of the 27 probes. These field soil temperatures were used to infer soil thermal diffusivity at numerous locations and depths in a discontinuous permafrost environment, and to evaluate the links between the estimated soil thermal diffusivity values and soil physical properties. The comma-delimited data files (.csv) of the synthetic and field soil temperature time series includes date and time (UTC) in the first column and soil temperature from 5 cm below the ground surface to 1.05 m depth (with 5 or 10 cm spacing between sensors) in the other columns. The measurements were acquired every 15 minutes. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The effect of varying Co layer thickness on the time-temperature characteristics of Co/Sb semimetal embedded magnetic nanoparticles

We report the effect of varying cobalt thickness on the temperature-dependent time decay of the electrical resistance of Co/Sb multilayer samples. We find that for a given temperature, a five fold change in the Co thickness produces a 100 fold change in the characteristic decay time of the resistance. We find that the characteristic decay time, as a function of temperature, follows an Arrhenius law. During deposition, the Co evolves single domain magnetic nanoparticles, on the Sb, in either a Volmer-Weber or Stranski-Krastanov growth mode. This metastable state is then encased in 2.5 nm of Sb producing an embedded nanoparticle system. Scanning tunneling microscopy measurements taken before sample aging (annealing at a given temperature for enough time to complete the resistance decay) and after aging show that these nanoparticles undergo morphological transformations during aging. These transformations lead to well defined time dependent decays in both the magnetization and the electrical resistance, making this material an excellent candidate for an electronic time-temperature sensor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Second-order statistical bootstrap for the uncertainty quantification of time-temperature-superposition analysis

Time-temperature superposition (TTS), which for decades has been a powerful method for long-term prediction from accelerated aging data, involves rigid-shifting isotherms in logarithmic time to produce a single master prediction curve. For simple thermo-rheological properties that accurately follow the TTS principle, the shifts can be easily determined, even manually by the eye. However, for many properties of interest, where the principle is obeyed only approximately, or the data is noisy, it is imperative to develop objective shifting techniques along with reliable uncertainty bounds. This work analyzes in detail the method of arclength-minimization as an unsupervised algorithm to determining optimum shifts and demonstrates that the method is nearly unbiased for all practical datasets with a variety of noise distributions. Moreover, if averaged over with-replacement (bootstrap) resamples, the predicted shifts follow a normal distribution, a fact that can be used to construct confidence interval for the master curve through a second-order bootstrap procedure.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Thermal Skin Damage During Reirradiation and Hyperthermia Is Time-Temperature Dependent

Purpose: To investigate the relationship of thermal skin damage (TSD) to time–temperature isoeffect levels for patients with breast cancer recurrence treated with reirradiation plus hyperthermia (reRT + HT), and to investigate whether the treatment history of previous treatments (scar tissue) is a risk factor for TSD. Methods and Materials: In this observational study, temperature characteristics of hyperthermia sessions were analyzed in 262 patients with recurrent breast cancer treated in the AMC between 2010 and 2014 with reirradiation and weekly hyperthermia for 1 hour. Skin temperature was measured using a median of 42 (range, 29-82) measurement points per hyperthermia session. Results: Sixty-eight patients (26%) developed 79 sites of TSD, after the first (n=26), second (n=17), third (n=27), and fourth (n=9) hyperthermia session. Seventy percent of TSD occurred on or near scar tissue. Scar tissue reached higher temperatures than other skin tissue (0.4°C, P<.001). A total of 102 measurement points corresponded to actual TSD sites in 35 of 79 sessions in which TSD developed. Thermal skin damage sites had much higher maximum temperatures than non-TSD sites (2.8°C, P<.001). Generalized linear mixed models showed that the probability of TSD is related to temperature and thermal dose values (P<.001) and that scar tissue is more at risk (odds ratio 0.4, P<.001). Limiting the maximum temperature of a measurement point to 43.7°C would mean that the probability of observing TSD was at most 5%. Conclusion: Thermal skin damage during reRT + HT for recurrent breast cancer was related to higher local temperatures and time–temperature isoeffect levels. Scar tissue reached higher temperatures than other skin tissue, and TSD occurred at lower temperatures and thermal dose values in scar tissue compared with other skin tissue. Indeed, TSD developed often on and around scar tissue from previous surgical procedures.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1979

Time-temperature behavior of a unidirectional graphite/epoxy composite

A testing program to determine the time-temperature response of unidirectional T300/934 graphite/epoxy materials is presented. The short-term creep test results of tension specimens with the load at various angles to the fiber direction and at various temperatures are reported, showing that the material is elastic at all temperatures when the fiber is in the load direction. However, when the load is transverse to the fibers, the viscoelastic response varies from small amounts at room temperature to large amounts at temperatures above the 180 C transition temperature. The time-temperature superposition principle or the method of reduced variables were used to determine compliance master curves for each fiber angle, and a viscoelastic analog to the elastic orthotropic transformation equation was used incrementally to predict the master curves for the tensile compliance of the off-axis specimen.

Yeow, Y. T.↗
DOE OSTI2012

Statistics of particle time-temperature histories: Progress report for August 2012

Progress toward predictions of the statistics of particle time-temperature histories is presented. The joint statistics of a scalar like the temperature and an exposure time scale are of interest. We have related these quantities to statistics of conserved scalars, their gradients and their diffusive time scales. Future work will apply the one-dimensional turbulence (ODT) model to evaluate the suitability of these statistics to predicting particle time-temperature histories. To that end, we have reported on past performance of the ODT model and have implemented a Lagrangian particle tracking capability into the ODT code. The results of ODT-driven particle dispersion have been compared with classical experimental data, and preliminary statistics of particle evolution in reacting flows are also reported.

Hewson, John C.↗
DOE OSTI2024

Interpreting test temperature and loading rate effects on the fracture toughness of polymer-metal interfaces via time–temperature superposition

Here, in this letter, we present interfacial fracture toughness data for a polymer-metal interface where tests were conducted at various test temperatures T and loading rates $\dot{δ}$. An adhesively bonded asymmetric double cantilever beam (ADCB) specimen was utilized to measure toughness. ADCB specimens were created by bonding a thinner, upper adherend to a thicker, lower adherend (both 6061 T6 aluminum) using a thin layer of epoxy adhesive, such that the crack propagated along the interface between the thinner adherend and the epoxy layer. The specimens were tested at T from 25 to 65 °C and $\dot{δ}$ from 0.002 to 0.2 mm/s. The measured interfacial toughness Γ increased as both T and $\dot{δ}$ increased. For an ADCB specimen loaded at a constant $\dot{δ}$, the energy release rate G increases as the crack length a increases. For this reason, we defined rate effects in terms of the rate of change in the energy release rate $\dot{G}$. Although not rigorously correct, a formal application of time–temperature superposition (TTS) analysis to the Γ data provided useful insights on the observed dependencies. In the TTS-shifted data, Γ decreased and then increased for monotonically increasing $\dot{G}$. Thus, the TTS analysis suggests that there is a minimum value of Γ. This minimum value could be used to define a lower bound in Γ when designing critical engineering applications that are subjected to T and $\dot{δ}$ excursions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Statistics of particle time-temperature histories

Particles in non-isothermal turbulent flow are subject to a stochastic environment that produces a distribution of particle time-temperature histories. This distribution is a function of the dispersion of the non-isothermal (continuous) gas phase and the distribution of particles relative to that gas phase. In this work we extend the one-dimensional turbulence (ODT) model to predict the joint dispersion of a dispersed particle phase and a continuous phase. The ODT model predicts the turbulent evolution of continuous scalar fields with a model for the cascade of fluctuations to smaller scales (the ‘triplet map’) at a rate that is a function of the fully resolved one-dimensional velocity field. Stochastic triplet maps also drive Lagrangian particle dispersion with finite Stokes numbers including inertial and eddy trajectory-crossing effects included. Two distinct approaches to this coupling between triplet maps and particle dispersion are developed and implemented along with a hybrid approach. An ‘instantaneous’ particle displacement model matches the tracer particle limit and provides an accurate description of particle dispersion. A ‘continuous’ particle displacement model translates triplet maps into a continuous velocity field to which particles respond. Particles can alter the turbulence, and modifications to the stochastic rate expression are developed for two-way coupling between particles and the continuous phase. Each aspect of model development is evaluated in canonical flows (homogeneous turbulence, free-shear flows and wall-bounded flows) for which quality measurements are available. ODT simulations of non-isothermal flows provide statistics for particle heating. These simulations show the significance of accurately predicting the joint statistics of particle and fluid dispersion. Inhomogeneous turbulence coupled with the influence of the mean flow fields on particles of varying properties alters particle dispersion. The joint particle-temperature dispersion leads to a distribution of temperature histories predicted by the ODT. Predictions are shown for the lower moments and the full distributions of the particle positions, particle-observed gas temperatures and particle temperatures. An analysis of the time scales affecting particle-temperature interactions covers Lagrangian integral time scales based on temperature autocorrelations, rates of temperature change associated with particle motion relative to the temperature field and rates of diffusional change of temperatures. These latter two time scales have not been investigated previously; they are shown to be strongly intermittent having peaked distributions with long tails. The logarithm of the absolute value of these time scales exhibits a distribution closer to normal.

Hewson, John C.↗
NASA NTRS2002

Prediction of Long-Term Strength of Thermoplastic Composites Using Time-Temperature Superposition

Accelerated tests for composite failure were investigated. Constant ramp transverse strength tests on thermoplastic composite specimens were conducted at four temperatures from 300 F to 450 F and five duration times from 0.5 sec to 24 hrs. Up to 400 F, the time-temperature-superposition method produces a master curve allowing strength at longer times to be estimated from strength tests conducted over shorter times but at higher temperatures. The shift factors derived from compliance tests applied well to the strength data. To explain why strength behaved similar to compliance, a viscoelastic fracture model was investigated based on the hypothesis that the work of fracture for crack initiation at some critical flaw remains constant with time and temperature. The model, which used compliance as input, was found to fit the strength data only if the critical fracture energy was allowed to vary with stress rate. Fracture tests using double cantilever beam specimens were conducted from 300 F to 450 F over time scales similar to the strength study. The toughness data showed a significant change with loading rate, less variation with temperature, did not form a master curve, and could not be correlated with the fracture model. Since the fracture model did not fit the fracture data, an alternative explanation based on the dilatational strain energy density was proposed. However the usefulness of this model is severely limited because it relies on a critical parameter which varies with loading rate.

Reeder, James R.↗